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Azure for Architects

You're reading from   Azure for Architects Create secure, scalable, high-availability applications on the cloud

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781839215865
Length 698 pages
Edition 3rd Edition
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Authors (3):
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Jack Lee Jack Lee
Author Profile Icon Jack Lee
Jack Lee
Ritesh Modi Ritesh Modi
Author Profile Icon Ritesh Modi
Ritesh Modi
Rithin Skaria Rithin Skaria
Author Profile Icon Rithin Skaria
Rithin Skaria
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Toc

Table of Contents (21) Chapters Close

Preface 1. Getting started with Azure 2. Azure solution availability, scalability, and monitoring FREE CHAPTER 3. Design pattern – Networks, storage, messaging, and events 4. Automating architecture on Azure 5. Designing policies, locks, and tags for Azure deployments 6. Cost management for Azure solutions 7. Azure OLTP solutions 8. Architecting secure applications on Azure 9. Azure Big Data solutions 10. Serverless in Azure – Working with Azure Functions 11. Azure solutions using Azure Logic Apps, Event Grid, and Functions 12. Azure Big Data eventing solutions 13. Integrating Azure DevOps 14. Architecting Azure Kubernetes solutions 15. Cross-subscription deployments using ARM templates 16. ARM template modular design and implementation 17. Designing IoT solutions 18. Azure Synapse Analytics for architects 19. Architecting intelligent solutions Index

The evolution of AI

AI is not a new field of knowledge. In fact, the technology is a result of decades of innovation and research. However, its implementation in previous decades was a challenge for the following reasons:

  1. Cost: AI experiments were costly in nature and there was no cloud technology. All the infrastructure was either purchased or hired from a third party. Experiments were also time-consuming to set up and immense skills were needed to get started. A large amount of storage and compute power was also required, which was generally missing in the community at large and held in the hands of just a few.
  2. Lack of data: There were hardly any smart handheld devices and sensors available generating data. Data was limited in nature and had to be procured, which again made AI applications costly. Data was also less reliable and there was a general lack of confidence in the data itself.
  3. Difficulty: AI algorithms were not documented enough and were primarily in the...
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